Balancing learning and structure in a neural network
Extending @mirror2image's comment, if you have a certain metric that allows you to measure how close the intended layer is to a low pass filter (something that compares its output with what a low pass filter would have produced, for example), the simplest way to achieve what you want would be to add a term in your loss function that calculates the value of this metric. This way, each time you do a training step, the network now is not only made to output the correct predictions but is also forced to do so while also keeping that specific layer's behavior as close to a low-pass filter as possible.
This is the most common way of tweaking the behavior of neural networks and is often encountered in many research papers.